Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5V-Turbo
~58
0/8 categorieso1-preview
72
Winner · 1/8 categoriesGLM-5V-Turbo· o1-preview
Pick o1-preview if you want the stronger benchmark profile. GLM-5V-Turbo only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
o1-preview is clearly ahead on the aggregate, 72 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
o1-preview's sharpest advantage is in agentic, where it averages 75.4 against 58. The single biggest benchmark swing on the page is BrowseComp, 51.9% to 79%.
o1-preview is the reasoning model in the pair, while GLM-5V-Turbo is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | GLM-5V-Turbo | o1-preview |
|---|---|---|
| Agentico1-preview wins | ||
| BrowseComp | 51.9% | 79% |
| OSWorld-Verified | 62.3% | 71% |
| BrowseComp-VL | 51.9% | — |
| OSWorld | 62.3% | — |
| AndroidWorld | 75.7% | — |
| WebVoyager | 88.5% | — |
| Terminal-Bench 2.0 | — | 77% |
| Coding | ||
| HumanEval | — | 86% |
| SWE-bench Verified | — | 65% |
| LiveCodeBench | — | 60% |
| SWE-bench Pro | — | 69% |
| Multimodal & Grounded | ||
| Design2Code | 94.8% | — |
| Flame-VLM-Code | 93.8% | — |
| Vision2Web | 31.0% | — |
| ImageMining | 30.7% | — |
| MMSearch | 72.9% | — |
| MMSearch-Plus | 30.0% | — |
| SimpleVQA | 78.2% | — |
| Facts-VLM | 58.6% | — |
| V* | 89.0% | — |
| MMMU-Pro | — | 72% |
| OfficeQA Pro | — | 80% |
| Reasoning | ||
| MuSR | — | 86% |
| BBH | — | 93% |
| LongBench v2 | — | 87% |
| MRCRv2 | — | 83% |
| Knowledge | ||
| MMLU | — | 92% |
| GPQA | — | 90% |
| SuperGPQA | — | 88% |
| MMLU-Pro | — | 80% |
| HLE | — | 32% |
| FrontierScience | — | 83% |
| SimpleQA | — | 88% |
| Instruction Following | ||
| IFEval | — | 88% |
| Multilingual | ||
| MGSM | — | 90% |
| MMLU-ProX | — | 86% |
| Mathematics | ||
| AIME 2023 | — | 94% |
| AIME 2024 | — | 96% |
| AIME 2025 | — | 95% |
| HMMT Feb 2023 | — | 90% |
| HMMT Feb 2024 | — | 92% |
| HMMT Feb 2025 | — | 91% |
| BRUMO 2025 | — | 93% |
| MATH-500 | — | 94% |
o1-preview is ahead overall, 72 to 58. The biggest single separator in this matchup is BrowseComp, where the scores are 51.9% and 79%.
o1-preview has the edge for agentic tasks in this comparison, averaging 75.4 versus 58. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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